Customer Churn Prediction in Banking Sector

Computer science solutions offer powerful tools to banks, insurance companies, and pension funds, which are able to predict possible churners and allow companies to take proactive actions to retain customers.

The financial industry has always tried to track customer interactions with the company to do extensive analysis. Although a vast amount of data and its diversity makes the task of finding early signs of possible churn difficult, modern machine-learning techniques solve this job very effectively. Our main specialty of machine learning and complex data analytics allows us to build models that find patterns in customer behavior and make predictions with high accuracy.

A typical solution includes the following:

  • Consulting and defining of objectives 
  • Data collection
  • Data preparation
  • Data pre-processing
  • Building analytical model
  • Evaluation and model improvement

Our models are normally based on millions of records and tens or hundreds of attributes. So a big part of a data analysis task is preparing the dataset itself. Once the preparation stage is finished, our engineers either start building the classification model, or undertake special additional research. In a number of cases, a rich dataset may allow extraction of more than churn rates. Apart from a list of customers with the highest churn rate, a company may want to get answers to questions like “What are the main drivers for churn?” As a churn-prediction model heavily depends on unique features of the company and the available dataset, in most cases, Bitrefine group offers a tailored solution.

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